[1] Dindaroglu F, Yetkiner E. Cone beam computed tomography in orthodontics[J]. Turkish Journal of Orthodontics, 29, 16-21(2016).

[2] Wu Q J, Li T R, Wu Q W et al. Adaptive radiation therapy: technical components and clinical applications[J]. The Cancer Journal, 17, 182-189(2011).

[3] Muresanu S, Almasan O, Hedesiu M et al. Artificial intelligence models for clinical usage in dentistry with a focus on dentomaxillofacial CBCT: a systematic review[J]. Oral Radiology, 39, 18-40(2023).

[4] Siewerdsen J H, Jaffray D A. Cone-beam computed tomography with a flat-panel imager: magnitude and effects of X-ray scatter[J]. Medical Physics, 28, 220-231(2001).

[5] Niu T Y, Zhu L. Overview of X-ray scatter in cone-beam computed tomography and its correction methods[J]. Current Medical Imaging Reviews, 6, 82-89(2010).

[6] Duan X J, Tang T X, Zhou Z Z et al. Scatter correction of cone-beam micro-nano computed tomography for turbine blades[J]. Chinese Journal of Scientific Instrument, 41, 39-47(2020).

[7] Rong J Y, Liu W L, Gao P et al. The review of scatter suppression methods in cone beam CT[J]. Computerized Tomography Theory and Applications, 25, 235-250(2016).

[8] Zhang W J, Huang G, Ding H N et al. Research progress of scattering artifact correction in medical cone-beam computed tomography imaging based on deep learning[J]. CT Theory and Applications, 32, 285-296(2023).

[9] Rührnschopf E P, Klingenbeck K. A general framework and review of scatter correction methods in X-ray cone-beam computerized tomography. Part 1: scatter compensation approaches[J]. Medical Physics, 38, 4296-4311(2011).

[10] Siewerdsen J H, Moseley D J, Bakhtiar B et al. The influence of antiscatter grids on soft-tissue detectability in cone-beam computed tomography with flat-panel detectors: antiscatter grids in cone-beam CT[J]. Medical Physics, 31, 3506-3520(2004).

[11] Siewerdsen J H, Jaffray D A. Optimization of X-ray imaging geometry (with specific application to flat-panel cone-beam computed tomography)[J]. Medical Physics, 27, 1903-1914(2000).

[12] Sorenson J A, Floch J. Scatter rejection by air gaps: an empirical model[J]. Medical Physics, 12, 308-316(1985).

[13] Sun M, Star-Lack J M. Improved scatter correction using adaptive scatter kernel superposition[J]. Physics in Medicine and Biology, 55, 6695-6720(2010).

[14] Xu Y, Bai T, Yan H et al. A practical cone-beam CT scatter correction method with optimized Monte Carlo simulations for image-guided radiation therapy[J]. Physics in Medicine and Biology, 60, 3567-3587(2015).

[15] Maslowski A, Wang A, Sun M S et al. Acuros CTS: a fast, linear Boltzmann transport equation solver for computed tomography scatter-part I: core algorithms and validation[J]. Medical Physics, 45, 1899-1913(2018).

[16] Nomura Y, Xu Q, Peng H et al. Modified fast adaptive scatter kernel superposition (mfASKS) correction and its dosimetric impact on CBCT-based proton therapy dose calculation[J]. Medical Physics, 47, 190-200(2020).

[17] Qin P S, Lin G Q, Li X et al. A correlated sampling-based Monte Carlo simulation for fast CBCT iterative scatter correction[J]. Medical Physics, 50, 1466-1480(2023).

[18] Niu T Y, Xu L, Ren Q et al. UBES: unified scatter correction using ultrafast Boltzmann equation solver for conebeam CT[J]. Computers in Biology and Medicine, 170, 108045(2024).

[19] Tang T X, Duan X J, Zhou Z Z et al. Scatter correction based on beam stop array for cone-beam micro-computed tomography[J]. Acta Optica Sinica, 39, 0834001(2019).

[20] Cui H H, Jiang X, Tang W et al. A practical and robust method for beam blocker-based cone beam CT scatter correction[J]. Physics in Medicine & Biology, 68, 045006(2023).

[21] Jiang Y K, Yang C L, Yang P F et al. Scatter correction of cone-beam CT using a deep residual convolution neural network (DRCNN)[J]. Physics in Medicine & Biology, 64, 145003(2019).

[22] Liang X, Chen L Y, Nguyen D et al. Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy[J]. Physics in Medicine & Biology, 64, 125002(2019).

[23] Zhang Y, Ding S G, Gong X C et al. Generating synthesized computed tomography from CBCT using a conditional generative adversarial network for head and neck cancer patients[J]. Technology in Cancer Research & Treatment, 21, 153303(2022).

[24] Zhang X R, Jiang Y K, Luo C et al. Image-based scatter correction for cone-beam CT using flip swin transformer U-shape network[J]. Medical Physics, 50, 5002-5019(2023).

[25] Piao Z, Deng W X, Huang S et al. Adaptive scatter kernel deconvolution modeling for cone-beam CT scatter correction via deep reinforcement learning[J]. Medical Physics, 51, 1163-1177(2024).

[26] Zhang L P, Liu A Z, Xiao J et al. Dual encoder fusion U-Net (DEFU-net) for cross-manufacturer chest X-ray segmentation[C], 9333-9339(2021).

[27] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[EB/OL]. https:∥arxiv.org/abs/1505.04597

[28] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C], 2261-2269(2017).

[29] Liu C J, Wang A, Zhang G Y et al. Source blur elimination in micro-CT using self-attention-based U-Net[J]. Acta Optica Sinica, 44, 0734002(2024).

[30] Liang H C, Lü J, Yu M K et al. Childhood pneumonia CT image segmentation network with prior graph convolution and transformer fusion[J]. Acta Optica Sinica, 44, 1610002(2024).

[31] Liu Z, Lin Y T, Cao Y et al. Swin transformer: hierarchical vision transformer using shifted windows[C], 9992-10002(2021).

[32] Li L, Tan C D, Liao M J et al. Analytic reconstruction for parallel translational computed tomography based on Radon inverse transform[J]. Acta Optica Sinica, 41, 0611003(2021).

[33] Guo L L, Wang Y R, Wang F C et al. Widely-activated network merging perceptual loss via discrete wavelet transform for image super-resolution[J]. International Journal of Machine Learning and Cybernetics, 14, 2793-2813(2023).

[34] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[M]. Medical image computing and computer-assisted intervention-MICCAI 2015, 9351, 234-241(2015).

[35] Zhang Y, Yue N, Su M Y et al. Improving CBCT quality to CT level using deep learning with generative adversarial network[J]. Medical Physics, 48, 2816-2826(2021).

[36] McCollough C. TU-FG-207A-04: overview of the low dose CT grand challenge[J]. Medical Physics, 43, 3759-3760(2016).